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arXiv · 2412.17357

AI-Driven Control of Chaos: A Transformer-Based Approach for Dynamical Systems

David Valle·Rubén Capeáns·Alexandre Wagemakers·Miguel A. F. Sanjuán
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Abstract

Chaotic behavior in dynamical systems poses a significant challenge in trajectory control, traditionally relying on computationally intensive physical models. We present a machine learning-based algorithm to compute the minimum control bounds required to confine particles within a region indefinitely, using only samples of orbits that iterate within the region before diverging. This model-free approach achieves high accuracy, with a mean squared error of $2.88 \times 10^{-4}$ and computation times in the range of seconds. The results highlight its efficiency and potential for real-time control of chaotic systems.

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BibTeXRIS

David Valle, Rubén Capeáns, Alexandre Wagemakers, Miguel A. F. Sanjuán. 2024-12-23. AI-Driven Control of Chaos: A Transformer-Based Approach for Dynamical Systems. https://arxiv.org/abs/2412.17357

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